The emerging concern about data privacy and security has motivated the proposal of
federated learning. Federated learning allows computing nodes to only synchronize the …

The emerging concern about data privacy and security has motivated the proposal of
federated learning, which allows nodes to only synchronize the locally-trained models …

While petabytes of data are generated each day by a number of independent computing
devices, only a few of them can be finally collected and used for deep learning (DL) due to …

Federated learning can achieve distributed machine learning without sharing privacy and
sensitive data of end devices. However, high concurrent access to cloud servers increases …

Amid growing concerns on data privacy, Federated Learning (FL) has emerged as a
promising privacy preserving distributed machine learning paradigm. Given that the FL …

Federated Learning (FL) provides a promising solution for preserving privacy in learning
shared models on distributed devices without sharing local data on a central server …

Federated Learning (FL) enables training a global model without sharing the decentralized
raw data stored on multiple devices to protect data privacy. Due to the diverse capacity of the …

Federated Learning is designed for multiple mobile devices to collaboratively train an
artificial intelligence model while preserving data privacy. Instead of collecting the raw …

Federated learning (FL) has emerged in edge computing to address the limited bandwidth
and privacy concerns of traditional cloud-based training. However, the existing FL …

Federated Learning is a machine learning scheme in which a shared prediction model can
be collaboratively learned by a number of distributed nodes using their locally stored data. It …